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  • Perspective
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How to design preclinical studies in nanomedicine and cell therapy to maximize the prospects of clinical translation

Nature Biomedical Engineering volume 2, pages 797–809 (2018)Cite this article

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Abstract

The clinical translation of promising products, technologies and interventions from the disciplines of nanomedicine and cell therapy has been slow and inefficient. In part, translation has been hampered by suboptimal research practices that propagate biases and hinder reproducibility. These include the publication of small and underpowered preclinical studies, suboptimal study design (in particular, biased allocation of experimental groups, experimenter bias and lack of necessary controls), the use of uncharacterized or poorly characterized materials, poor understanding of the relevant biology and mechanisms, poor use of statistics, large between-model heterogeneity, absence of replication, lack of interdisciplinarity, poor scientific training in study design and methods, a culture that does not incentivize transparency and sharing, poor or selective reporting, misaligned incentives and rewards, high costs of materials and protocols, and complexity of the developed products, technologies and interventions. In this Perspective, we discuss special manifestations of these problems in nanomedicine and in cell therapy, and describe mitigating strategies. Progress on reducing bias and enhancing reproducibility early on ought to enhance the translational potential of biomedical findings and technologies.

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Fig. 1: Examples of heterogeneity and potential bias arising at each step of the preclinical research process in nanomedicine.
Fig. 2: Cell therapy for Parkinson’s disease (PD).

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References

  1. Bowen, A. & Casadevall, A. Increasing disparities between resource inputs and outcomes, as measured by certain health deliverables, in biomedical research. Proc. Natl Acad. Sci. USA 112, 11335–11340 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Contopoulos-Ioannidis, D. G., Ntzani, E. & Ioannidis, J. P. Translation of highly promising basic science research into clinical applications. Am. J. Med. 114, 477–484 (2003).

    PubMed  Google Scholar 

  3. Baker, M. 1,500 scientists lift the lid on reproducibility. Nature 533, 452–454 (2016).

    CAS  PubMed  Google Scholar 

  4. Allison, D. B., Brown, A. W., George, B. J. & Kaiser, K. A. Reproducibility: a tragedy of errors. Nature 530, 27–29 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Lithgow, G. J., Driscoll, M. & Phillips, P. A long journey to reproducible results. Nature 548, 387–388 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Bissell, M. Reproducibility: the risks of the replication drive. Nature 503, 333–334 (2013).

    PubMed  Google Scholar 

  7. Ioannidis, J. P. The reproducibility wars: successful, unsuccessful, uninterpretable, exact, conceptual, triangulated, contested replication. Clin. Chem. 63, 943–945 (2017).

    CAS  PubMed  Google Scholar 

  8. Ioannidis, J. P. Acknowledging and overcoming nonreproducibility in basic and preclinical research. JAMA 317, 1019–1020 (2017).

    PubMed  Google Scholar 

  9. Prinz, F., Schlange, T. & Asadullah, K. Believe it or not: how much can we rely on published data on potential drug targets? Nat. Rev. Drug Discov. 10, 712 (2011).

    CAS  PubMed  Google Scholar 

  10. Begley, C. G. & Ellis, L. M. Drug development: raise standards for preclinical cancer research. Nature 483, 531–533 (2012).

    CAS  PubMed  Google Scholar 

  11. Nosek, B. A. & Errington, T. M. Making sense of replications. eLife 6, e23383 (2017).

    PubMed  PubMed Central  Google Scholar 

  12. Begley, C. G. & Ioannidis, J. P. Reproducibility in science: improving the standard for basic and preclinical research. Circ. Res. 116, 116–126 (2015).

    CAS  PubMed  Google Scholar 

  13. Button, K. S. et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14, 365–376 (2013).

    CAS  PubMed  Google Scholar 

  14. Szucs, D. & Ioannidis, J. P. Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature. PLoS Biol. 15, e2000797 (2017).

    PubMed  PubMed Central  Google Scholar 

  15. Tsilidis, K. K. et al. Evaluation of excess significance bias in animal studies of neurological diseases. PLoS Biol. 11, e1001609 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Hess, K. R. Statistical design considerations in animal studies published recently in Cancer Research. Cancer Res. 71, 625 (2011).

    CAS  PubMed  Google Scholar 

  17. Kilkenny, C. et al. Survey of the quality of experimental design, statistical analysis and reporting of research using animals. PLoS ONE 4, e7824 (2009).

    PubMed  PubMed Central  Google Scholar 

  18. Steward, O., Popovich, P. G., Dietrich, W. D. & Kleitman, N. Replication and reproducibility in spinal cord injury research. Exp. Neurol. 233, 597–605 (2012).

    PubMed  Google Scholar 

  19. Szucs, D. & Ioannidis, J. P. A. When null hypothesis significance testing is unsuitable for research: a reassessment. Front. Hum. Neurosci. 11, 390 (2017).

    PubMed  PubMed Central  Google Scholar 

  20. Greenland, S. et al. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur. J. Epidemiol. 31, 337–350 (2016).

    PubMed  PubMed Central  Google Scholar 

  21. Benjamin, D. J. et al. Redefine statistical significance. Nat. Hum. Behav. 2, 6–10 (2018).

    PubMed  Google Scholar 

  22. Bruns, S. B. & Ioannidis, J. P. p-curve and p-hacking in observational research. PLoS ONE 11, e0149144 (2016).

    PubMed  PubMed Central  Google Scholar 

  23. Veresoglou, S. D. P hacking in biology: an open secret. Proc. Natl Acad. Sci. USA 112, E5112–E5113 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Fanelli, D. How many scientists fabricate and falsify research? A systematic review and meta-analysis of survey data. PLoS ONE 4, e5738 (2009).

    PubMed  PubMed Central  Google Scholar 

  25. Freedman, L. P., Cockburn, I. M. & Simcoe, T. S. The economics of reproducibility in preclinical research. PLoS Biol. 13, e1002165 (2015).

    PubMed  PubMed Central  Google Scholar 

  26. Ioannidis, J. P. A. et al. Increasing value and reducing waste in research design, conduct, and analysis. Lancet 383, 166–175 (2014).

    PubMed  PubMed Central  Google Scholar 

  27. Kimmelman, J., Mogil, J. S. & Dirnagl, U. Distinguishing between exploratory and confirmatory preclinical research will improve translation. PLoS Biol. 12, e1001863 (2014).

    PubMed  PubMed Central  Google Scholar 

  28. Simeon-Dubach, D., Burt, A. D. & Hall, P. A. Quality really matters: the need to improve specimen quality in biomedical research. J. Pathol. 228, 431–433 (2012).

    PubMed  Google Scholar 

  29. Dirnagl, U. et al. A concerted appeal for international cooperation in preclinical stroke research. Stroke 44, 1754–1760 (2013).

    PubMed  PubMed Central  Google Scholar 

  30. Goodman, S. N. Introduction to Bayesian methods I: measuring the strength of evidence. Clin. Trials 2, 282–290 (2005).

    PubMed  Google Scholar 

  31. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

    Google Scholar 

  32. Wasserstein, R. L. & Lazar, N. A. The ASA’s statement on p-values: context, process, and purpose. Am. Statistician 70, 129–133 (2016).

    Google Scholar 

  33. Colquhoun, D. The reproducibility of research and the misinterpretation of p-values. R. Soc. Open Sci. 4, 171085 (2017).

    PubMed  PubMed Central  Google Scholar 

  34. Colquhoun, D. The false positive risk: a proposal concerning what to do about p values. Preprint at https://arXiv.org/abs/1802.04888 (2018).

  35. Ioannidis, J. P. The proposal to lower P value thresholds to .005. JAMA 319, 1429–1430 (2018).

    PubMed  Google Scholar 

  36. Nosek, B. A. et al. Promoting an open research culture. Science 348, 1422–1425 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Stodden, V. et al. Enhancing reproducibility for computational methods. Science 354, 1240–1241 (2016).

    CAS  PubMed  Google Scholar 

  38. Simera, I., Moher, D., Hoey, J., Schulz, K. F. & Altman, D. G. A catalogue of reporting guidelines for health research. Eur. J. Clin. Invest. 40, 35–53 (2010).

    CAS  PubMed  Google Scholar 

  39. Henderson, V. C., Kimmelman, J., Fergusson, D., Grimshaw, J. M. & Hackam, D. G. Threats to validity in the design and conduct of preclinical efficacy studies: a systematic review of guidelines for in vivo animal experiments. PLoS Med. 10, e1001489 (2013).

    PubMed  PubMed Central  Google Scholar 

  40. Chambers, C. D., Dienes, Z., McIntosh, R. D., Rotshtein, P. & Willmes, K. Registered reports: realigning incentives in scientific publishing. Cortex 66, A1–A2 (2015).

    PubMed  Google Scholar 

  41. Moher, D., Goodman, S. N. & Ioannidis, J. P. Academic criteria for appointment, promotion and rewards in medical research: where’s the evidence? Eur. J. Clin. Invest. 46, 383–385 (2016).

    PubMed  Google Scholar 

  42. Ioannidis, J. P. & Khoury, M. J. Assessing value in biomedical research: the PQRST of appraisal and reward. JAMA 312, 483–484 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Jackman, J. A., Lee, J. & Cho, N. J. Nanomedicine for infectious disease applications: innovation towards broad-spectrum treatment of viral infections. Small 12, 1133–1139 (2016).

    CAS  PubMed  Google Scholar 

  44. Barranco, C. Nanomedicine, meet autoimmune disease. Nat. Rev. Rheum. 12, 193 (2016).

    CAS  Google Scholar 

  45. Clemente-Casares, X. et al. Expanding antigen-specific regulatory networks to treat autoimmunity. Nature 530, 434–440 (2016).

    CAS  PubMed  Google Scholar 

  46. Wilhelm, S. et al. Analysis of nanoparticle delivery to tumours. Nat. Rev. Mater. 1, 16014 (2016).

    CAS  Google Scholar 

  47. Shi, J., Kantoff, P. W., Wooster, R. & Farokhzad, O. C. Cancer nanomedicine: progress, challenges and opportunities. Nat. Rev. Cancer 17, 20–37 (2017).

    CAS  PubMed  Google Scholar 

  48. Jiang, W. et al. Designing nanomedicine for immuno-oncology. Nat. Biomed. Eng. 1, 0029 (2017).

    Google Scholar 

  49. Blanco, E., Shen, H. & Ferrari, M. Principles of nanoparticle design for overcoming biological barriers to drug delivery. Nat. Biotechnol. 33, 941–951 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. von Roemeling, C. A., Jiang, W., Chan, C. K., Weissman, I. L. & Kim, B. Y. S. Breaking down the barriers to precision cancer nanomedicine. Trends Biotechnol. 35, 159–171 (2017).

    Google Scholar 

  51. Rolland, J. P., Hagberg, E. C., Denison, G. M., Carter, K. R. & De Simone, J. M. High-resolution soft lithography: enabling materials for nanotechnologies. Angew. Chem. Int. Ed. 43, 5796–5799 (2004).

    CAS  Google Scholar 

  52. Xu, J. et al. Future of the particle replication in nonwetting templates (PRINT) technology. Angew. Chem. Int. Ed. 52, 6580–6589 (2013).

    CAS  Google Scholar 

  53. Peer, D. et al. Nanocarriers as an emerging platform for cancer therapy. Nat. Nanotech. 2, 751–760 (2007).

    CAS  Google Scholar 

  54. Rodriguez, P. L. et al. Minimal “self” peptides that inhibit phagocytic clearance and enhance delivery of nanoparticles. Science 339, 971–975 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Monopoli, M. P., Åberg, C., Salvati, A. & Dawson, K. A. Biomolecular coronas provide the biological identity of nanosized materials. Nat. Nanotech. 7, 779–786 (2012).

    CAS  Google Scholar 

  56. Albanese, A., Tang, P. S. & Chan, W. C. W. The effect of nanoparticle size, shape, and surface chemistry on biological systems. Annu. Rev. Biomed. Eng. 14, 1–16 (2012).

    CAS  PubMed  Google Scholar 

  57. Walkey, C. D., Olsen, J. B., Guo, H., Emili, A. & Chan, W. C. W. Nanoparticle size and surface chemistry determine serum protein adsorption and macrophage uptake. J. Am. Chem. Soc. 134, 2139–2147 (2012).

    CAS  PubMed  Google Scholar 

  58. Salvati, A. et al. Transferrin-functionalized nanoparticles lose their targeting capabilities when a biomolecule corona adsorbs on the surface. Nat. Nanotech. 8, 137–143 (2013).

    CAS  Google Scholar 

  59. Jiang, W., Kim, B. Y. S., Rutka, J. T. R. & Chan, W. C. W. Nanoparticle-mediated cellular response is size-dependent. Nat. Nanotech. 3, 145–150 (2008).

    CAS  Google Scholar 

  60. Rice, S. B. et al. Particle size distributions by transmission electron microscopy: an interlaboratory comparison case study. Metrologia 50, 663–678 (2013).

    PubMed  PubMed Central  Google Scholar 

  61. Krystek, P., Ulrich, A., Garcia, C. C., Manohar, S. & Ritsema, R. Application of plasma spectrometry for the analysis of engineered nanoparticles in suspensions and products. J. Anal. Atom. Spectrom. 26, 1701–1721 (2011).

    CAS  Google Scholar 

  62. Masters, J. R. Cell-line authentication: end the scandal of false cell lines. Nature 492, 186 (2012).

    CAS  PubMed  Google Scholar 

  63. Allen, M., Bjerke, M., Edlund, H., Nelander, S. & Westermark, B. Origin of the U87MG glioma cell line: good news and bad news. Sci. Transl. Med. 8, 354re3 (2016).

    PubMed  Google Scholar 

  64. Pollard, S. M. et al. Glioma stem cell lines expanded in adherent culture have tumor-specific phenotypes and are suitable for chemical and genetic screens. Cell Stem Cell 4, 568–580 (2009).

    CAS  PubMed  Google Scholar 

  65. Tsuchiya, S. et al. Establishment and characterization of a human acute monocytic leukemia cell line (THP-1). Int. J. Cancer 26, 171–176 (1980).

    CAS  PubMed  Google Scholar 

  66. Prideaux, B. & Stoeckli, M. Mass spectrometry imaging for drug distribution studies. J. Proteomics 75, 4999–5013 (2012).

    CAS  PubMed  Google Scholar 

  67. Limberis, M. P., Bell, C. L. & Wilson, J. M. Identification of the murine firefly luciferase-specific CD8 T-cell epitopes. Gene Therapy 16, 441–447 (2009).

    CAS  PubMed  Google Scholar 

  68. Baklaushev, V. P. et al. Luciferase expression allows bioluminescence imaging but imposes limitations on the orthotopic mouse (4T1) model of breast cancer. Sci. Rep. 7, 7715 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Dimmeler, S., Ding, S., Rando, T. A. & Trounson, A. Translational strategies and challenges in regenerative medicine. Nat. Med. 20, 814–821 (2014).

    CAS  PubMed  Google Scholar 

  70. Rennert, R. C. et al. High-resolution microfluidic single-cell transcriptional profiling reveals clinically relevant subtypes among human stem cell populations commonly utilized in cell-based therapies. Front. Neurology 7, 41 (2016).

    Google Scholar 

  71. Ortmann, D. & Vallier, L. Variability of human pluripotent stem cell lines. Curr. Opin. Genet. Dev. 46, 179–185 (2017).

    CAS  PubMed  Google Scholar 

  72. Paladino, F. V., Sardinha, L. R., Piccinato, C. A. & Goldberg, A. C. Intrinsic variability present in Wharton’s jelly mesenchymal stem cells and T cell responses may impact cell therapy. Stem Cells Int. 2017, 8492797 (2017).

    PubMed  PubMed Central  Google Scholar 

  73. Bianco, P. “Mesenchymal” stem cells. Annu. Rev. Cell Dev. Biol. 30, 677–704 (2014).

    CAS  PubMed  Google Scholar 

  74. Merkle, F. T. et al. Human pluripotent stem cells recurrently acquire and expand dominant P53 mutations. Nature 545, 229–233 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Trounson, A. Potential pitfall of pluripotent stem cells. N. Engl. J. Med. 377, 490–491 (2017).

    PubMed  Google Scholar 

  76. Wang, X. et al. Tumor suppressor gene alterations of spontaneously transformed cells from human embryonic muscle in vitro. Oncol. Rep. 24, 555–561 (2010).

    CAS  PubMed  Google Scholar 

  77. Tang, C., Weissman, I. L. & Drukker, M. The safety of embryonic stem cell therapy relies on teratoma removal. Oncotarget 3, 7–8 (2012).

    PubMed  PubMed Central  Google Scholar 

  78. Barker, R. A. et al. Are stem cell-based therapies for Parkinson’s disease ready for the clinic in 2016? J. Parkinson’s Dis. 6, 57–63 (2016).

    Google Scholar 

  79. Kriks, S. et al. Floor plate-derived dopamine neurons from hESCs efficiently engraft in animal models of PD. Nature 480, 547–551 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Gonzalez, R. et al. Neural stem cells from human parthenogenetic stem cells engraft and promote recovery in a nonhuman primate model of Parkinson’s disease. Cell Transplant. 25, 1945–1966 (2016).

    PubMed  Google Scholar 

  81. Wagers, A. J. & Weissman, I. L. Plasticity of adult stem cells. Cell 116, 639–648 (2004).

    CAS  PubMed  Google Scholar 

  82. Kikuchi, T. et al. Human iPS cell-derived dopaminergic neurons function in a primate Parkinson’s disease model. Nature 548, 592–596 (2017).

    CAS  PubMed  Google Scholar 

  83. Schulz, T. C. Concise review: manufacture of pancreatic endoderm cells for clinical trials in type 1 diabetes. Stem Cells Transl. Med. 4, 927–931 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Vegas, A. J. et al. Long term glycemic control using polymer encapsulated human stem-cell derived β-cells in immune competent mice. Nat. Med. 22, 306–311 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Brudno, J. N. & Kochenderfer, J. N. Chimeric antigen receptor T-cell therapies for lymphoma. Nat. Rev. Clin. Oncol. 15, 31–46 (2018).

    CAS  PubMed  Google Scholar 

  86. Hombach, A. A. & Abken, H. Most do, but some do not: CD4+CD25– T cells, but not CD4+CD25+ Treg cells, are cytolytic when redirected by chimeric antigen receptor (CAR). Cancers (Basel) 9, 112 (2017).

    Google Scholar 

  87. Temple, S. & Studder, L. Lessons learned from pioneering neural stem cell studies. Stem Cell Rep. 8, 191–193 (2017).

    Google Scholar 

  88. Prestwich, G. D. et al. What is the greatest regulatory challenge in the translation of biomaterials to the clinic? Sci. Transl. Med. 4, 160cm14 (2012).

    PubMed  Google Scholar 

  89. Trounson, A. & McDonald, C. Stem cell therapies in clinical trials: progress and challenges. Cell Stem Cell 17, 11–22 (2015).

    CAS  PubMed  Google Scholar 

  90. Karnik, R. et al. Microfluidic platform for controlled synthesis of polymeric nanoparticles. Nano Lett. 8, 2906–2912 (2008).

    CAS  PubMed  Google Scholar 

  91. Tropsha, A., Mills, K. C. & Hickey, A. J. Reproducibility, sharing and progress in nanomaterial databases. Nat. Nanotech. 12, 1111–1114 (2017).

    CAS  Google Scholar 

  92. Rolland, J. P. et al. Direct fabrication and harvesting of monodisperse, shape-specific nanobiomaterials. J. Am. Chem. Soc. 127, 10096–10100 (2005).

    CAS  PubMed  Google Scholar 

  93. Huh, D. et al. Reconstituting organ-level lung functions on a chip. Science 328, 1662–1668 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Albanese, A., Lam, A. K., Sykes, E. A., Rocheleau, J. V. & Chan, W. C. Tumour-on-a-chip provides an optical window into nanoparticle tissue transport. Nat. Commun. 4, 2718 (2013).

    PubMed  Google Scholar 

  95. Rongvaux, A. et al. Development and function of human innate immune cells in a humanized mouse model. Nat. Biotechnol. 32, 364–372 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Liu, Q., Shepherd, B. E., Li, C. & Harrell, F. E. Jr Modeling continuous response using ordinal regression. Stat. Med. https://doi.org/10.1002/sim.7433 (2017).

    PubMed  PubMed Central  Google Scholar 

  97. Barish, S., Ochs, M. F., Sontag, E. D. & Gevertz, J. L. Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy. Proc. Natl Acad. Sci. USA 114, E6277–E6286 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Lin-Gibson, S., Sarkar, S. & Ito, Y. Defining quality attributes to enable measurement assurance for cell therapy products. Cytotherapy 18, 1241–1244 (2016).

    PubMed  Google Scholar 

  99. Maus, M. V. & Kikiforow, S. The why, what, and how of the new FACT standards for immune effector cells. J. Immunother. Cancer 5, 36 (2017).

    PubMed  PubMed Central  Google Scholar 

  100. Dropulic, B. Reference standards for gene and cell therapy products. Mol. Therapy 25, 1259–1260 (2017).

    CAS  Google Scholar 

  101. Stacey, G. N. et al. Preservation and stability of cell therapy products: recommendations from an expert workshop. Regen. Med. 12, 553–564 (2017).

    CAS  PubMed  Google Scholar 

  102. Williams, D. J. et al. Comparability: manufacturing, characterization and controls, report of a UK regenerative medicine platform pluripotent stem cell platform workshop, Trinity Hall, Cambridge, 14-15 September 2015. Regen. Med. 11, 483–492 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Rama, P., Ferrari, G. & Pellegrini, G. Cultivated limbal epithelial transplantation. Curr. Opin. Ophthalmol. 28, 387–389 (2017).

    PubMed  Google Scholar 

  104. Wei, G., Wang, J., Huang, H. & Zhao, Y. Novel immunotherapies for adult patients with B-lineage acute lymphoblastic leukemia. J. Hemat. Oncol. 10, 150 (2017).

    Google Scholar 

  105. Brudno, J. N. & Kochenderfer, J. N. Toxicities of chimeric antigen receptor T cells: recognition and management. Blood 127, 3321–3330 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. Benjamin, D., Mandel, D. R. & Kimmelman, J. Can cancer researchers accurately judge whether preclinical reports will reproduce? PLoS Biol. 15, e2002212 (2017).

    PubMed  PubMed Central  Google Scholar 

  107. Fesnak, A., June, C. H. & Levine, B. L. Engineered T cells: the promise and challenges of cancer immunotherapy. Nat. Rev. Cancer 16, 566–681 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. Trounson, A., DeWitt, N. D. & Feigal, E. G. The alpha stem cell clinic: a model for evaluating and delivering stem cell-based therapies. Stem Cells Transl. Med. 1, 9–14 (2012).

    PubMed  PubMed Central  Google Scholar 

  109. Lomax, G. P. et al. Accelerating stem cell treatments for patients: the value of networks and collaborations. Stem Cells Portal http://stemcellsportal.com/content/2015-0090 (2015).

  110. Mayo-Wilson, E., Doshi, P. & Dickersin, K. Are manufacturers sharing data as promised?. Br. Med. J. 351, h4169 (2015).

    Google Scholar 

  111. Guinney, J. et al. Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data. Lancet Oncol. 18, 132–142 (2017).

    PubMed  Google Scholar 

  112. Corrie, B. D. et al. iReceptor: a platform for querying and analysing antibody/B-cell and T-cell receptor repertoire data across federated repositories. Immunol. Rev. 284, 24–41 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. Clevers, H. Modeling development and disease with organoids. Cell 165, 1586–1597 (2016).

    CAS  PubMed  Google Scholar 

  114. Esch, E. W., Bahinski, A. & Huh, D. Organs-on-chips at the frontiers of drug discovery. Nat. Rev. Drug Discov. 14, 248–260 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. Verma, S. et al. Trastuzumab emtansine for HER2-positive advanced breast cancer. N. Engl. J. Med. 367, 1783–1791 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. Lewis Phillips, G. D. et al. Targeting HER2-positive breast cancer with trastuzumab-DM1, an antibody-cytotoxic drug conjugate. Cancer Res. 68, 9280–9290 (2008).

    CAS  PubMed  Google Scholar 

  117. Erickson, H. K. et al. Antibody-maytansinoid conjugates are activated in targeted cancer cells by lysosomal degradation and linker-dependent intracellular processing. Cancer Res. 66, 4426–4433 (2006).

    CAS  PubMed  Google Scholar 

  118. Holden, S. N. et al. A phase I study of weekly dosing of trastuzumab-DM1 (T-DM1) in patients (pts) with advanced HER2+ breast cancer. J. Clin. Oncol. 26, 1029 (2008).

    Google Scholar 

  119. Vieweg, J. et al. Immunotherapy of prostate cancer in the Dunning rat model: use of cytokine gene modified tumor vaccines. Cancer Res. 54, 1760–1765 (1994).

    CAS  PubMed  Google Scholar 

  120. Hurwitz, A. A. et al. Combination immunotherapy of primary prostate cancer in a transgenic mouse model using CTLA-4 blockade. Cancer Res. 60, 2444–2448 (2000).

    CAS  PubMed  Google Scholar 

  121. Kantoff, P. W. et al. Sipuleucel-T immunotherapy for castration-resistant prostate cancer. N. Engl. J. Med. 363, 411–422 (2010).

    CAS  PubMed  Google Scholar 

  122. Higano, C. S. et al. Integrated data from 2 randomized, double-blind, placebo-controlled, phase 3 trials of active cellular immunotherapy with sipuleucel-T in advanced prostate cancer. Cancer 115, 3670–3679 (2009).

    CAS  PubMed  Google Scholar 

  123. Gong, C. L. & Hay, J. W. Cost-effectiveness analysis of abiraterone and sipuleucel-T in asymptomatic metastatic castration-resistant prostate cancer. J. Natl Compr. Canc. Netw. 12, 1417–1425 (2014).

    PubMed  Google Scholar 

  124. Geynisman, D. M., Chien, C. R., Smieliauskas, F., Shen, C. & Shih, Y. C. Economic evaluation of therapeutic cancer vaccines and immunotherapy: a systematic review. Hum. Vaccin. Immunother. 10, 3415–3424 (2014).

    PubMed  PubMed Central  Google Scholar 

  125. Jarosławski, S. & Toumi, M. Sipuleucel-T (Provenge)-autopsy of an innovative paradigm change in cancer treatment: why a single-product biotech company failed to capitalize on its breakthrough invention. BioDrugs 29, 301–307 (2015).

    PubMed  Google Scholar 

  126. Simpson, E. L., Davis, S., Thokala, P., Breeze, P. R., Bryden, P. & Wong, R. Sipuleucel-T for the treatment of metastatic hormone-relapsed prostate cancer: a NICE single technology appraisal; an evidence review group perspective. Pharmacoeconomics 33, 1187–1194 (2015).

    PubMed  Google Scholar 

Download references

Acknowledgements

We thank N. Boyd for helping create Fig. 2. We also acknowledge funding from the Mayo Clinic Center for Regenerative Medicine (B.Y.S.K.), the National Institute of Neurological Disorders and Stroke Grant R01 NS104315 (B.Y.S.K.) and the Laura and John Arnold Foundation for providing funding for the Meta-Research Innovation Center at Stanford (METRICS) (J.P.A.I.).

Author information

Authors and Affiliations

  1. Departments of Medicine, Health Research and Policy, and Biomedical Data Science, Stanford University School of Medicine, and Department of Statistics, Stanford University School of Humanities and Sciences, and Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, USA

    John P. A. Ioannidis

  2. Departments of Neurosurgery, Cancer Biology, and Neuroscience, Mayo Clinic College of Medicine, Jacksonville, FL, USA

    Betty Y. S. Kim

  3. Monash University & Hudson Institute of Medical Research, Clayton, Victoria, Australia

    Alan Trounson

Authors
  1. John P. A. Ioannidis
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  2. Betty Y. S. Kim
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  3. Alan Trounson
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All authors contributed to writing the paper, revising it and approving the final version.

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Correspondence to John P. A. Ioannidis.

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Ioannidis, J.P.A., Kim, B.Y.S. & Trounson, A. How to design preclinical studies in nanomedicine and cell therapy to maximize the prospects of clinical translation. Nat Biomed Eng 2, 797–809 (2018). https://doi.org/10.1038/s41551-018-0314-y

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